Data processing method, apparatus, device and storage medium

ABSTRACT

A data processing method and apparatus, a device and a storage medium, where the method includes: obtaining first target result data according to an original ultrasonic echo signal, where the first target result data includes a related parameter of a detected object; performing feature extraction on the first target result data using a pre-trained feature extraction model to obtain second target result data; and performing corresponding processing on the detected object based on the second target result data. By performing feature extraction on the related parameter of the detected object using the pre-trained feature extraction model to obtain the second target result data, and further performing corresponding processing on the detected object based on the second target result data, thereby improving accuracy of judging the state of the detected object effectively.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2020/105006, filed on Jul. 28, 2020, which claims priority toChinese Patent Application No. 201910706620.X, filed on Aug. 1, 2019,both of the applications are hereby incorporated by reference in theirentireties.

TECHNICAL FIELD

The present application relates to the technical field of ultrasoundimage processing and, in particular, to a data processing method andapparatus, a device and a storage medium.

BACKGROUND

With the advancement of science and technology, ultrasound imagingtechnology is widely used in various fields. In prior art, in general,after an original ultrasonic echo signal is acquired, it is necessary toperform image reconstruction and image processing to obtain some relatedparameters of a detected object, such as a velocity, a direction, etc.,and judge a state of the detected object according to these relatedparameters.

However, accuracy of judging the state of the detected object in theprior art is relatively low, which gradually cannot meet an accuracyrequirement for ultrasonic detection of the detected object. Therefore,how to accurately judge the state of the detected object has become atechnical problem that needs to be solved urgently.

SUMMARY

The present application provides a data processing method and apparatus,a device, and a storage medium to solve disadvantages of low judgmentaccuracy in the prior art

A first aspect of the present application provides a data processingmethod, including:

obtaining first target result data according to an original ultrasonicecho signal, where the first target result data includes a relatedparameter of a detected object;

performing feature extraction on the first target result data using apre-trained feature extraction model to obtain second target resultdata; and

performing corresponding processing on the detected object based on thesecond target result data.

A second aspect of the present application provides a data processingapparatus, including:

a first processing module, configured to obtain first target result dataaccording to an original ultrasonic echo signal, where the first targetresult data includes a related parameter of a detected object;

a second processing module, configured to perform feature extraction onthe first target result data using a pre-trained feature extractionmodel to obtain second target result data;

and a third processing module, configured to perform correspondingprocessing on the detected object based on the second target resultdata.

A third aspect of the present application provides a computer device,including: at least one processor and a memory;

where the memory stores a computer program; and the at least oneprocessor executes the computer program stored in the memory toimplement the method provided in the first aspect.

A fourth aspect of the present application provides a computer-readablestorage medium in which a computer program is stored, and the methodprovided in the first aspect is implemented when the computer program isexecuted.

According to the data processing method and apparatus, device, andstorage medium provided in the present application, by performingfeature extraction on a related parameter of a detected object using apre-trained feature extraction model to obtain second target resultdata, and further performing corresponding processing on the detectedobject based on the second target result data, accuracy of judging thestate of the detected object can be improved effectively.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly describe the technical solution in embodimentsof the present application or the prior art, in the following, thedrawings that need to be used in the description of the embodiments orthe prior art will be introduced briefly. Apparently, the drawings inthe following description are a part of embodiments of the presentapplication. For persons of ordinary skill in the art, other drawingscan be obtained based on these drawings without paying creative labor.

FIG. 1 is a schematic structural diagram of a data processing system towhich an embodiment of the present application is applicable;

FIG. 2 is a schematic flowchart of a data processing method provided byan embodiment of the present application;

FIG. 3 is a schematic flowchart of a data processing method provided byanother embodiment of the present application;

FIG. 4 is a schematic structural diagram of a data processing apparatusprovided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a data processing systemprovided by another embodiment of the present application; and

FIG. 6 is a schematic structural diagram of a computer device providedby an embodiment of the present application.

Through the above drawings, specific embodiments of the presentapplication have been shown, which will be described in more detailbelow. These drawings and descriptions are not intended to limit thescope of the concept of the present disclosure in any way, but toexplain the concept of the present application to the persons skilled inthe art by referring to specific embodiments.

DESCRIPTION OF EMBODIMENTS

In order to make the purpose, the technical solution, and the advantageof embodiments of the present application clearer, the technicalsolution in embodiments of the present application will be clearly andcompletely described below with reference to the accompanying drawings.Apparently, the described embodiments are merely a part rather than allembodiments of the present application. All other embodiments obtainedby the persons of ordinary skill in the art based on embodiments in thepresent application without paying creative labor shall fall within theprotection scope of the present application.

Firstly, terms involved in the present application will be explained.

Image reconstruction refers to the technology of obtaining shapeinformation of a three-dimensional object through digital processing ofdata measured outside of an object. Image reconstruction technology maybe used in radiological medical equipment to display images of variousparts of a human body, that is, the computed tomography technology, orCT technology for short. And it may also be applied in other fields.

Image processing refers to the technology of analyzing an image with acomputer to achieve a desired result. In the embodiments of the presentapplication, it refers to perform image post-processing and signalextraction on a reconstructed result image to improve image clarity andhighlight image features, and obtain a related parameter of a detectedobject, such as a velocity, a direction, an acceleration, a strain, astrain rate, an elastic modulus and other quantitative parameters of thedetected object, etc.

The data processing method provided by the embodiments of the presentapplication is applicable to the following data processing system. Asshown in FIG. 1, it is a schematic structural diagram of a dataprocessing system to which an embodiment of the present application isapplicable. The data processing system includes a cloud computingplatform, a data collecting system and a display system. The datacollecting system is responsible for collecting data to be processed,where the data to be processed may include a collected originalultrasonic echo signal. The cloud computing platform is responsible forperforming corresponding processing on the data to be processed toobtain a required result. The display system is responsible fordisplaying related data or the result obtained during the processing ofthe cloud computing platform. The data processing system may alsoinclude a local computing platform for sharing part of processing tasksof the cloud computing platform.

The terms “first”, “second”, etc. are only used for descriptivepurposes, and cannot be understood as indicating or implying relativeimportance or implicitly indicating a number of indicated technicalfeatures. In the description of the following embodiments, “multiple”means two or more, unless otherwise specifically defined.

The following specific embodiments can be combined with each other, andthe same or similar concepts or processes may not be repeated in someembodiments. The embodiments of the present application will bedescribed below in conjunction with the drawings.

Embodiment I

This embodiment provides a data processing method for processing anultrasonic echo signal to obtain required result data. An executionsubject of this embodiment is a data processing apparatus, which may beset in a cloud computing platform. Or the apparatus may be partly set ina local computing platform, and other parts are set in the cloudcomputing platform.

As shown in FIG. 2, it is a schematic flowchart of a data processingmethod provided by this embodiment, and the method includes:

step 101: obtaining first target result data according to an originalultrasonic echo signal, where the first target result data includes arelated parameter of a detected object.

Specifically, the original ultrasonic echo signal may be obtained from adata collecting terminal, or may be collected and stored in advance,such as stored in a cloud computing platform, or stored in a localcomputing platform and sent to the cloud computing platform when neededfor processing, or processed by the local computing platform, etc., andthe specific obtaining method is not limited. After the originalultrasonic echo signal is acquired, the first target result data may beobtained according to the original ultrasonic echo signal, where thefirst target result data includes related parameters of the detectedobject, such as related parameters representing a moving velocity (suchas a velocity of a blood flow), a moving direction (such as a directionof the blood flow), an elasticity (such as a strain, a strain rate,etc.) of the detected object, which may specifically include adisplacement, a velocity, an acceleration, a strain, a strain rate, anelastic modulus and other quantitative parameters, etc. The first targetresult data may also include parameters related to image features, suchas a contrast, a texture feature, and other quantitative parameters, andmay also include information such as a distribution feature ofscatterers, a density of the scatterers, and a size of the scatterers.There are no specific restrictions. The first target result data may bein a form of data or in a form of an image, such as a pseudo-colorimage.

The detected object may be human or animal tissues such as a liver, akidney, a spleen, or other objects in the air or geology, which may bedetermined according to actual needs, and is not limited in theembodiment of the present application.

In an implementation, processing such as image reconstruction and imageprocessing may be performed on the original ultrasonic echo signal toobtain the first target result data. The specific processing method maybe the prior art, which is not limited in this embodiment.

Step 102, performing feature extraction on the first target result datausing a pre-trained feature extraction model to obtain second targetresult data.

Specifically, the pre-trained feature extraction model may be a machinelearning model or an artificial intelligence model, where the trainingof the feature extraction model may be performed using a large amount ofpre-collected training data and labeled data obtained from labeling thetraining data. The specific training process is consistent with thetraining process of an existing neural network model, which will not berepeated here. Types of parameters included in the training data areconsistent with those in the first target result data, such as differentvelocities of a blood flow, directions of a blood flow, and elasticityinformation. The labeled data may be texture features, uniformity, etc.,or the labeled data may also be a state of the detected objectcorresponding to the training data, such as whether it is liverfibrosis, cirrhosis and its specific staging, whether it is fatty liverand its specific staging, whether it is tumor and benign or malignant.The detail may be set according to actual needs.

The trained feature extraction model may perform feature extraction andresult prediction based on the first target result data to obtain thesecond target result data, where the second target result data may be animage texture feature, uniformity and other features of the detectedobject, and may also be a state feature of the detected object obtainedafter feature analysis and weighting of these features, such as whetherthe detected object is liver fibrosis, cirrhosis and its specificstaging, fatty liver and its specific staging, tumor and benign ormalignant, etc. Here, the state features output by the model may belabels corresponding to different states, for example, 0 means “normal”,1 means “fatty liver”, etc., and the detail may be set according toactual needs, which is not limited in this embodiment.

In an implementation, at least two models such as a machine learningmodel and an artificial intelligence model may be used in parallel forfeature extraction, and the results of each model are synthesized toobtain the second target result data. For example, three differentmodels are used for feature extraction, if the state features of thedetected object are acquired, where the results of two models are “1”and the result of one model is “0”, the result should be “1” followingthe principle of “the minority is subordinate to the majority”, however,this is only an exemplary description rather than a limitation.

Step 103, performing corresponding processing on the detected objectbased on the second target result data.

Specifically, after the second target result data is obtained,corresponding processing may be performed on the detected object basedon the second target result data. For example, judging the state of thedetected object based on the state feature of the detected object. Foranother example, displaying the state of the detected object, ordisplaying the second target result data of the detected object, etc.The second target result data may assist a related person to understandthe state of the detected object. Such as assisting a doctor indiagnosis and so on.

In an implementation, the method provided in this embodiment may beexecuted by a cloud computing platform, or may be executed by a localcomputing platform, or partly executed by a local computing platform andpartly executed by a cloud computing platform, and the detail may be setaccording to actual needs, which is not limited in this embodiment.

The data processing method provided in this embodiment, by performingfeature extraction on the related parameter of the detected object usingthe pre-trained feature extraction model to obtain the second targetresult data, and further performing corresponding processing on thedetected object based on the second target result data, combining thedetection with a neural network, improves the judgment accuracy of thestate of the detected object effectively.

Embodiment II

This embodiment further supplements the method provided in Embodiment I.

As shown in FIG. 3, it is a schematic flowchart of the data processingmethod provided by this embodiment.

As an implementable manner, on the basis of the above Embodiment I, inan implementation, step 101 specifically includes:

step 1011: performing image reconstruction on the original ultrasonicecho signal to obtain a target reconstruction result image.

Specifically, after the original ultrasonic echo signal is acquired,image reconstruction needs to be performed on the original ultrasonicecho signal to obtain the target reconstruction result image, such as anultrasound image, a B-mode ultrasonic image, etc. The targetreconstruction result image may be in the form of radio frequency,envelope, grayscale, etc.

Step 1012: performing image processing on the target reconstructionresult image to obtain first target result data.

Specifically, after the target reconstruction result image is obtained,image processing needs to be performed on the target reconstructionresult image to improve image clarity and highlight image features. Forexample, grayscale correction, grayscale expansion and compression, γcorrection, histogram equalization, electronic amplification,interpolation processing, etc., are performed. Finally, the relatedparameter of the detected object, that is, the first target result datais obtained. The specific image processing method may be set accordingto actual needs, which is not limited here.

In an implementation, step 1011 may specifically include:

step 10111: performing image reconstruction on the original ultrasonicecho signal using a spatial point-based image reconstruction algorithmto obtain a first reconstruction result image, where the spatialpoint-based image reconstruction algorithm is an image reconstructionalgorithm compatible with multiple types of probes; and taking the firstreconstruction result image as the target reconstruction result image.

In an implementation, the performing image reconstruction on theoriginal ultrasonic echo signal using the spatial point-based imagereconstruction algorithm to obtain the first reconstruction result imageincludes:

performing, according to pre-configured parameters of a probe and adisplay parameter, image reconstruction on the original ultrasonic echosignal using the spatial point-based image reconstruction algorithm toobtain the first reconstruction result image, where the parameters ofthe probe include an identifier of the probe, a Cartesian coordinatezero point of the probe, and a first coordinate of each array element ofthe probe, and the display parameter includes a second coordinate of thefirst reconstruction result image.

Specifically, the spatial point-based image reconstruction algorithmincludes: pre-defined parameters of a probe, that is, a probe is definedin a unified format according to physical parameters of the probe toform a probe parameter index table, where the probe parameter indextable is composed of an identification code of a type of the probe(i.e., the identifier of the probe), a Cartesian coordinate zero pointof the probe and a coordinate position of each element of the probe(i.e., the first coordinate), a type of the probe currently used may beidentified by the identification code, and the parameters of the probemay be searched in the probe parameter index table. In animplementation, a probe defining module may be set to manage theparameters of the probe. It is also necessary to define the displayparameter of the reconstructed image, different display parameters maybe defined for different types of probes, and image reconstruction isperformed according to the display parameter, so as to be compatiblewith multiple types of probes. The display parameter is composed of adefinition of a coordinate range, a coordinate position (Xi, Yi, Zi) ora pixel size (ΔXi, ΔYi, ΔZi) of a target image (that is, the targetreconstruction result image). In an implementation, an image definingmodule may be set to manage the display parameter. A probe identifyingmodule may also be set to identify the probe. The types of probe includelinear array, convex array, phased array, two-dimensional area array andother types.

Due to different application scenarios of ultrasound probes, differenttypes of probes have different shapes, sizes and responsecharacteristics. In general, the probe is composed of multiple arrayelements, and the arrangement and size of the array elements have animpact on the image reconstruction algorithm.

During image reconstruction, the propagation path L(i) of ultrasound atany point P(i) (refers to a point corresponding to the coordinateposition (Xi, Yi, Zi) in the above target image) in space is:L(i)=L(t)+P(Xi,Yi,Zi)−P(Xt,Yt,Zt), t=1, 2, 3 . . . n, n≥1, where n isthe number of array elements of the probe. Furthermore, adaptive beamcombination is realized (adaptation here refers to performing accordingto different coordinate requirements. The specific method may be anexisting technology, such as delay overlaying, etc.). Among them, thecoordinate zero point of the probe is a middle position of the probe(X0, Y0, Z0), the coordinate position of each element of the probe is(Xt, Yt, Zt), and a center plane of an imaging plane of the probe is theXZ plane, the plane which is perpendicular to the imaging plane of theprobe and parallel to a tangent plane at the zero position of the probeis the XY plane.

Take the convex array probe as an example (not limited to the convexarray probe): a position, a center frequency, a bandwidth and otherparameters of the convex array probe are written into the probe definingmodule; a specific probe code is programed by using several pins of theconvex array probe, the probe identifying module may identify the probecode when the probe is connected to the data processing system, and mayfurther search the related parameters in the probe defining module; thedisplay mode of the image (that is, the display parameter) is defined inthe image defining module, and image reconstruction is performedaccording to such mode. This image reconstruction method is suitable forany probe, that is, it realizes ultrasound image reconstructioncompatible with multiple types of probes, thereby improving theflexibility and efficiency of image reconstruction.

In some implementation manners, step 1012 may specifically include:

step 10121: performing image post-processing and signal extraction onthe target reconstruction result image to obtain the first target resultdata, where the first target result data includes at least one of adisplacement, a velocity, an acceleration, a strain, a strain rate, anelastic modulus, a contrast, a texture feature, a distribution featureof scatterers, a density of scatterers, and a size of scatterers.

Specifically, after the target reconstruction result image is obtained,image post-processing and signal extraction are performed on the targetreconstruction result image to obtain the first target result data, suchas Doppler, elasticity calculation, etc. If the above imagereconstruction algorithm compatible with multiple types of probes isused in image reconstruction, the image processing may also becompatible with multiple types of probes, and the probe defining module,the probe identifying module, and the image defining module are stillused. The probe identifying module identifies the type of the probecurrently used by designing an identification code of the probe, andsearches the parameters of the probe in the index table; the displayparameter is defined in the image defining module, and imagereconstruction is performed based on this parameter; and the imagedefining module performs image processing to obtain a data processingresult (that is, the first target result data) that does not depend onthe type of the probe, thereby realizing the compatibility of multipletypes of probes.

Among them, image post-processing and signal extraction are the processof image processing, the image processing in this embodiment includesthe whole process of image post-processing and signal extraction. Forexample, when a convex array is used for Doppler signal processing (asignal extraction method in the step of image processing), if a signalobtained by using a traditional image reconstruction algorithm is alongan emission direction of the convex array (fan beam), when Dopplersignal extraction is performed, the obtained direction of the blood flowis also along the emission direction of the convex array. If thedistribution of the velocity of the blood flow in the horizontal orvertical direction in a Cartesian coordinate system is required, it canonly be obtained by getting a component along a corresponding angle.While when adopting the image processing method in the embodiment of thepresent application, it is possible to directly obtain the distributionof the velocity of the blood flow in the horizontal or verticaldirection in the Cartesian coordinate system (specifically, thedistribution can be obtained by using autocorrelation, short timeFourier transform and other existing technologies based on the firsttarget result data). In the same way, the method is also applicable toarray elements of other types of probe, such as phased array and areaarray.

In some implementation manners, the performing image reconstruction onthe original ultrasonic echo signal to obtain the target reconstructionresult image includes:

in step 2011, for each probe, performing, according to an imagereconstruction algorithm corresponding to a type of the probe, imagereconstruction on the original ultrasonic echo signal to obtain a secondreconstruction result image.

Specifically, image reconstruction is performed on each probe accordingto the respective image reconstruction algorithm configured to obtainthe second reconstruction result image.

Here, a solution is provided when image reconstruction algorithms ofmultiple types of probes are not compatible, image reconstruction foreach probe is performed according to the respective image reconstructionalgorithm configured, that is, different types of probes may need toadopt different image reconstruction algorithms, a corresponding imagereconstruction algorithm may be configured for a respective type ofprobe, and the image reconstruction algorithm corresponding to the probeis determined according to the type of the probe to perform imagereconstruction after using different types of probes to collect theoriginal ultrasonic echo signal. The specific reconstruction method isthe existing technology, which will not be repeated here.

Step 2012: performing spatial interpolation processing on the secondreconstruction result image to obtain a third reconstruction resultimage, and taking the third reconstruction result image as the targetreconstruction result image.

Specifically, in order to obtain the target reconstruction result imagecompatible with different types of probes, it is necessary to performspatial interpolation processing on the second reconstruction resultimage to obtain the third reconstruction result image which may be usedas the target reconstruction result image.

The third reconstruction result image obtained through spatialinterpolation processing is substantially equivalent to the firstreconstruction result image obtained by the above spatial point-basedimage reconstruction algorithm. The difference is that the effects areslightly different, where the first reconstruction result image isobtained by direct reconstruction, and the third reconstruction resultimage is obtained by interpolating the traditional reconstructionresult. Spatial interpolation processing may be implemented in a varietyof ways, such as linear interpolation, non-linear interpolation and etc.

In an implementation, after the performing image processing on thetarget reconstruction result image to obtain the first target resultdata, the method may further include:

step 2021: performing digital scan conversation on the first targetresult data to obtain converted result data.

Step 2022: performing display processing on the converted result data.

Specifically, the obtained first target result data may also be used toassist in diagnosing and have certain reference significance. Therefore,the first target result data may be displayed, however, it needs to bedisplayed after digital scan conversion. Therefore, it is necessary toperform digital scan conversion on the first target result data toobtain the converted result data, and then perform display processing onthe converted result data.

In some implementation manners, step 103 may specifically include:

step 1031: judging a state of the detected object based on the secondtarget result data.

Exemplarily, judging, according to the second target result data,whether the detected object is liver fibrosis, cirrhosis and itsspecific staging, fatty liver and its specific staging, tumor and benignor malignant, etc.

In an implementation, the method may further include:

step 104: performing display processing on the state of the detectedobject.

In some implementation manners, after the obtaining the first targetresult data according to the original ultrasonic echo signal, the methodfurther includes:

step 203: judging a state of the detected object based on the firsttarget result data.

The obtained first target result data may also be used to assist indiagnosing and have certain reference significance, therefore, the stateof the detected object may be judged based on the first target resultdata. For example, thresholds of different parameters and levels of theparameters may be set, where different levels correspond to differentstates of the detected object, etc., and the details will not berepeated here.

In some implementation manners, the method in the embodiment of thepresent application is executed by the cloud computing platform.

In other implementation manners, the local computing platform obtainsthe first target result data according to the original ultrasonic echosignal, and sends the first target result data to the cloud computingplatform; the cloud computing platform performs feature extraction onthe first target result data using the pre-trained feature extractionmodel to obtain the second target result data, and performscorresponding processing on the detected object based on the secondtarget result data. That is, step 101 is executed by the local computingplatform, and steps 102-103 are processed by the cloud computingplatform.

It should be noted that each implementable manner in this embodiment canbe implemented separately, or can be implemented in any combinationwithout confliction, which is not limited in the present application.

The data processing method provided in this embodiment, by performingfeature extraction on a related parameter of a detected object using apre-trained feature extraction model to obtain the second target resultdata, and further performing corresponding processing on the detectedobject based on the second target result data, thereby improvingaccuracy of judging the state of the detected object effectively.Furthermore, by performing image reconstruction using a spatialpoint-based image reconstruction algorithm which can be compatible withmultiple types of probes, thereby improving the flexibility andefficiency of image reconstruction. Furthermore, by performing imageprocessing based on a target reconstruction result image compatible withmultiple types of probes, thereby improving the accuracy of the relatedparameter of the detected object. Both the obtained first target resultdata and the second target result data may be used to assist a relatedperson in diagnosing the detected object, thereby improving thediagnosis efficiency.

Embodiment III

This embodiment provides a data processing apparatus for executing themethod in the above Embodiment I.

As shown in FIG. 4, it is a schematic structural diagram of a dataprocessing apparatus provided by this embodiment. The data processingapparatus 30 includes a first processing module 31, a second processingmodule 32 and a third processing module 33.

Among them, the first processing module 31 is configured to obtain firsttarget result data according to an original ultrasonic echo signal,where the first target result data includes a related parameter of adetected object; the second processing module 32 is configured toperform feature extraction on the first target result data using apre-trained feature extraction model to obtain second target resultdata; and the third processing module 33 is configured to performcorresponding processing on the detected object based on the secondtarget result data.

Regarding the apparatus in this embodiment, the specific manners forperforming operations by each module have been described in detail inthe embodiment related to the method, and detailed description will notbe given here.

According to the data processing apparatus provided in this embodiment,by performing feature extraction on a related parameter of a detectedobject using a pre-trained feature extraction model to obtain secondtarget result data, and further performing corresponding processing onthe detected object based on the second target result data, therebyimproving accuracy of judging the state of the detected objecteffectively.

Embodiment IV

This embodiment further supplements the apparatus provided in the aboveEmbodiment III.

As an implementable manner, on the basis of the above embodiment III, inan implementation, the first processing module is specificallyconfigured to:

perform image reconstruction on the original ultrasonic echo signal toobtain a target reconstruction result image; and

perform image processing on the target reconstruction result image toobtain the first target result data.

In some implementation manners, the first processing module isspecifically configured to:

perform image reconstruction on the original ultrasonic echo signalusing a spatial point-based image reconstruction algorithm to obtain afirst reconstruction result image, where the spatial point-based imagereconstruction algorithm is an image reconstruction algorithm compatiblewith multiple types of probes; and take the first reconstruction resultimage as the target reconstruction result image.

In some implementation manners, the first processing module isspecifically configured to:

perform, according to pre-configured parameters of a probe and a displayparameter, image reconstruction on the original ultrasonic echo signalusing the spatial point-based image reconstruction algorithm to obtainthe first reconstruction result image; where the parameters of the probeinclude an identifier of the probe, a Cartesian coordinate zero point ofthe probe, and a first coordinate of each array element of the probe,and the display parameter includes a second coordinate of the firstreconstruction result image.

In some implementation manners, the first processing module isspecifically configured to:

perform image post-processing and signal extraction on the targetreconstruction result image to obtain the first target result data,where the first target result data includes at least one of adisplacement, a velocity, an acceleration, a strain, a strain rate, anelastic modulus, a contrast, a texture feature, a distribution featureof scatterers, a density of scatterers, and a size of scatterers.

In some implementation manners, the first processing module isspecifically configured to:

perform, based on an image reconstruction algorithm which is notcompatible with multiple types of probes, image reconstruction on theoriginal ultrasonic echo signal to obtain a second reconstruction resultimage;

perform spatial interpolation processing on the second reconstructionresult image to obtain a third reconstruction result image; and

take the third reconstruction result image as the target reconstructionresult image.

In an implementation, the first processing module is further configuredto:

perform digital scan conversation on the first target result data toobtain converted result data; and

perform display processing on the converted result data.

As another implementable manner, on the basis of the above EmbodimentIII, in an implementation, the third processing module is specificallyconfigured to:

judge a state of the detected object based on the second target resultdata.

In some implementation manners, the third processing module is furtherconfigured to:

perform display processing on the state of the detected object.

As another implementable manner, on the basis of the above EmbodimentIII, in an implementation, the first processing module is furtherconfigured to judge a state of the detected object based on the firsttarget result data.

Regarding the apparatus in this embodiment, the specific manners forperforming operations by each module have been described in detail inthe embodiment related to the method, and detailed description will notbe given here.

It should be noted that each implementable manner in this embodiment canbe implemented separately, or can be implemented in any combinationwithout confliction, which is not limited in the present application.

According to the data processing apparatus of this embodiment, byperforming feature extraction on a related parameter of a detectedobject using a pre-trained feature extraction model to obtain secondtarget result data, and further performing corresponding processing onthe detected object based on the second target result data, therebyimproving accuracy of judging the state of the detected objecteffectively. Furthermore, by performing image reconstruction using aspatial point-based image reconstruction algorithm which can becompatible with multiple types of probes, thereby improving theflexibility and efficiency of image reconstruction. Furthermore, byperforming image processing based on a target reconstruction resultimage compatible with multiple types of probes, thereby improving theaccuracy of the related parameter of the detected object. Both theobtained first target result data and the second target result data maybe used to assist a related person in diagnosing the detected object,thereby improving the diagnosis efficiency.

In some embodiments, the data processing system may include a datacollecting system, a local computing platform, a cloud computingplatform, and a display system. As shown in FIG. 5, it is a schematicstructural diagram of a data processing system provided by thisembodiment. The first processing module in the data processing apparatusis set in a local computing platform, and the second processing moduleand the third processing module in the data processing apparatus are setin the cloud computing platform.

Embodiment V

This embodiment provides a computer device for executing the methodprovided in the above embodiment. The computer device may be the abovecloud computing platform, or may include the above cloud computingplatform and a local computing platform. Specifically, it may be adesktop computer, a notebook computer, a server, and other computerdevice.

As shown in FIG. 6, it is a schematic structure diagram of the computerdevice provided by this embodiment. The computer device 50 includes: atleast one processor 51 and a memory 52;

where the memory stores a computer program; and the at least oneprocessor executes the computer program stored in the memory toimplement the method provided in the above embodiments.

According to the computer device of this embodiment, by performingfeature extraction on a related parameter of a detected object using apre-trained feature extraction model to obtain second target resultdata, and further performing corresponding processing on the detectedobject based on the second target result data, thereby improvingaccuracy of judging the state of the detected object effectively.Furthermore, by performing image reconstruction using a spatialpoint-based image reconstruction algorithm which can be compatible withmultiple types of probes, thereby improving the flexibility andefficiency of image reconstruction. Furthermore, by performing imageprocessing based on a target reconstruction result image compatible withmultiple types of probes, thereby improving the accuracy of the relatedparameter of the detected object. Both the obtained first target resultdata and the second target result data may be used to assist a relatedperson in diagnosing the detected object, thereby improving thediagnosis efficiency.

Embodiment VI

This embodiment provides a computer-readable storage medium in which acomputer program is stored, and the method provided in any of the aboveembodiments is implemented when the computer program is executed.

According to the computer-readable storage medium of this embodiment, byperforming feature extraction on a related parameter of a detectedobject using a pre-trained feature extraction model to obtain secondtarget result data, and further performing corresponding processing onthe detected object based on the second target result data, therebyimproving accuracy of judging the state of the detected objecteffectively. Furthermore, by performing image reconstruction using aspatial point-based image reconstruction algorithm which can becompatible with multiple types of probes, thereby improving theflexibility and efficiency of image reconstruction. Furthermore, byperforming image processing based on a target reconstruction resultimage compatible with multiple types of probes, thereby improving theaccuracy of the related parameter of the detected object. Both theobtained first target result data and the second target result data maybe used to assist a related person in diagnosing the detected object,thereby improving the diagnosis efficiency.

In the several embodiments provided in the present application, itshould be understood that the disclosed apparatus and method may beimplemented in other ways. For example, the apparatus embodimentsdescribed above are merely illustrative, for example, the division ofthe units is only a logical function division, and there may be otherdivisions in actual implementation. For example, multiple units orcomponents may be combined or integrated into another system, or somefeatures may be omitted or not implemented. In addition, the displayedor discussed mutual coupling or direct coupling or communicationconnection may be indirect coupling or communication connection throughsome interfaces, apparatus or units, and may be in electrical,mechanical or other forms.

The units described as separate components may or may not be physicallyseparated, and the components displayed as units may or may not bephysical units, that is, they may be located in one place, or they maybe distributed on multiple network units. Some or all of the units maybe selected according to actual needs to achieve the objectives of thesolutions of the embodiments.

In addition, the functional units in the various embodiments of thepresent application may be integrated into one processing unit, or eachunit may exist alone physically, or two or more units may be integratedinto one unit. The above integrated unit may be implemented in the formof hardware, or may be implemented in the form of hardware with asoftware functional unit.

The above integrated unit implemented in the form of a softwarefunctional unit may be stored in a computer readable storage medium. Theabove software functional unit is stored in the storage medium andincludes several instructions to enable a computer device (which may bea personal computer, a server, or a network device, etc.) or a processorto execute a part of the steps of the method described in eachembodiment of the present application. The above storage mediumincludes: a U disk, a portable hardisk, a read-only memory (ROM), arandom access memory (RAM), a magnetic disk or an optical disk and othermediums that can store program codes.

The persons skilled in the art clearly understands that, the division ofthe above functional modules is only used as an example for theconvenience and conciseness of the description. In practicalapplications, the above functions may be allocated by differentfunctional modules according to needs, that is, the internal structureof the apparatus is divided into different functional modules tocomplete all or part of the functions described above. For the specificworking process of the apparatus described above, reference may be madeto the corresponding process in the above method embodiment, which willnot be repeated here.

Finally, it should be noted that the above embodiments are only used toillustrate the technical solutions of the present application ratherthan limiting them; although the present application has been describedin detail with reference to the above embodiments, the persons ofordinary skill in the art should understand that: it is still possibleto modify the technical solutions recorded in the above embodiments, orequivalently replace some or all of the technical features; however,these modifications or replacements do not cause the essence of thecorresponding technical solutions to deviate from the scope of thetechnical solutions of the embodiments of the present application.

What is claimed is:
 1. A data processing method, comprising: obtainingfirst target result data according to an original ultrasonic echosignal, wherein the first target result data comprises a relatedparameter of a detected object; performing feature extraction on thefirst target result data using a pre-trained feature extraction model toobtain second target result data; and performing correspondingprocessing on the detected object based on the second target resultdata.
 2. The method according to claim 1, wherein the obtaining thefirst target result data according to the original ultrasonic echosignal comprises: performing image reconstruction on the originalultrasonic echo signal to obtain a target reconstruction result image;and performing image processing on the target reconstruction resultimage to obtain the first target result data.
 3. The method according toclaim 2, wherein the performing image reconstruction on the originalultrasonic echo signal to obtain the target reconstruction result imagecomprises: performing image reconstruction on the original ultrasonicecho signal using a spatial point-based image reconstruction algorithmto obtain a first reconstruction result image, wherein the spatialpoint-based image reconstruction algorithm is an image reconstructionalgorithm compatible with multiple types of probes; and taking the firstreconstruction result image as the target reconstruction result image.4. The method according to claim 3, wherein the performing imagereconstruction on the original ultrasonic echo signal using the spatialpoint-based image reconstruction algorithm to obtain the firstreconstruction result image comprises: performing, according topre-configured parameters of a probe and a display parameter, imagereconstruction on the original ultrasonic echo signal using the spatialpoint-based image reconstruction algorithm to obtain the firstreconstruction result image, wherein the parameters of the probecomprise an identifier of the probe, a Cartesian coordinate zero pointof the probe, and a first coordinate of each array element of the probe,and the display parameter comprises a second coordinate of the firstreconstruction result image.
 5. The method according to claim 2, whereinthe performing image processing on the target reconstruction resultimage to obtain the first target result data comprises: performing imagepost-processing and signal extraction on the target reconstructionresult image to obtain the first target result data, wherein the firsttarget result data comprises at least one of a displacement, a velocity,an acceleration, a strain, a strain rate, an elastic modulus, acontrast, a texture feature, a distribution feature of scatterers, adensity of scatterers, and a size of scatterers.
 6. The method accordingto claim 2, wherein the performing image reconstruction on the originalultrasonic echo signal to obtain the target reconstruction result imagecomprises: for each probe, performing, according to an imagereconstruction algorithm corresponding to a type of the probe, imagereconstruction on the original ultrasonic echo signal to obtain a secondreconstruction result image; performing spatial interpolation processingon the second reconstruction result image to obtain a thirdreconstruction result image; and taking the third reconstruction resultimage as the target reconstruction result image.
 7. The method accordingto claim 6, wherein after the performing image processing on the targetreconstruction result image to obtain the first target result data, themethod further comprises: performing digital scan conversation on thefirst target result data to obtain converted result data; and performingdisplay processing on the converted result data.
 8. The method accordingto claim 1, wherein the performing corresponding processing on thedetected object based on the second target result data comprises:judging a state of the detected object based on the second target resultdata.
 9. The method according to claim 8, further comprising: performingdisplay processing on the state of the detected object.
 10. The methodaccording to claim 1, wherein after the obtaining the first targetresult data according to the original ultrasonic echo signal, the methodfurther comprises: judging a state of the detected object based on thefirst target result data.
 11. A computer device, comprising: at leastone processor and a memory; wherein the memory stores a computerprogram; and the at least one processor executes the computer programstored in the memory to: obtain first target result data according to anoriginal ultrasonic echo signal, wherein the first target result datacomprises a related parameter of a detected object; perform featureextraction on the first target result data using a pre-trained featureextraction model to obtain second target result data; and performcorresponding processing on the detected object based on the secondtarget result data.
 12. The device according to claim 11, wherein the atleast one processor is specifically configured to: perform imagereconstruction on the original ultrasonic echo signal to obtain a targetreconstruction result image; and perform image processing on the targetreconstruction result image to obtain the first target result data. 13.The device according to claim 12, wherein the at least one processor isspecifically configured to: perform image reconstruction on the originalultrasonic echo signal using a spatial point-based image reconstructionalgorithm to obtain a first reconstruction result image, wherein thespatial point-based image reconstruction algorithm is an imagereconstruction algorithm compatible with multiple types of probes; andtake the first reconstruction result image as the target reconstructionresult image.
 14. The device according to claim 13, wherein the at leastone processor is specifically configured to: perform, according topre-configured parameters of a probe and a display parameter, imagereconstruction on the original ultrasonic echo signal using the spatialpoint-based image reconstruction algorithm to obtain the firstreconstruction result image; wherein the parameters of the probecomprise an identifier of the probe, a Cartesian coordinate zero pointof the probe, and a first coordinate of each array element of the probe,and the display parameter comprises a second coordinate of the firstreconstruction result image.
 15. The device according to claim 12,wherein the at least one processor is specifically configured to:perform image post-processing and signal extraction on the targetreconstruction result image to obtain the first target result data,wherein the first target result data comprises at least one of adisplacement, a velocity, an acceleration, a strain, a strain rate, anelastic modulus, a contrast, a texture feature, a distribution featureof scatterers, a density of scatterers, and a size of scatterers. 16.The device according to claim 12, wherein the at least one processor isspecifically configured to: for each probe, perform, according to animage reconstruction algorithm corresponding to a type of the probe,image reconstruction on the original ultrasonic echo signal to obtain asecond reconstruction result image; perform spatial interpolationprocessing on the second reconstruction result image to obtain a thirdreconstruction result image; and take the third reconstruction resultimage as the target reconstruction result image.
 17. The deviceaccording to claim 16, wherein the at least one processor is furtherconfigured to: perform digital scan conversation on the first targetresult data to obtain converted result data; and perform displayprocessing on the converted result data.
 18. The device according toclaim 11, wherein the at least one processor is specifically configuredto: judge a state of the detected object based on the second targetresult data.
 19. The device according to claim 18, wherein the at leastone processor is further configured to: perform display processing onthe state of the detected object.
 20. A non-transitory computer-readablestorage medium, wherein a computer program is stored in thecomputer-readable storage medium, and the computer program, whenexecuted, cause a computer to: obtain first target result data accordingto an original ultrasonic echo signal, wherein the first target resultdata comprises a related parameter of a detected object; perform featureextraction on the first target result data using a pre-trained featureextraction model to obtain second target result data; and performcorresponding processing on the detected object based on the secondtarget result data.